Descrizione del progetto
Processo decisionale basato sull’intelligenza artificiale per le operazioni di sistemi critici
L’intelligenza artificiale (IA) può essere un potente strumento per la gestione di sistemi critici tradizionalmente sotto il controllo umano. Il progetto AI4REALNET, finanziato dall’UE, svilupperà metodi per dare priorità all’affidabilità nel controllo umano assistito dall’IA, incorporando la cognizione aumentata, il co-apprendimento ibrido uomo-IA e l’IA autonoma, il tutto mantenendo l’attenzione sulla resilienza, la sicurezza e la protezione delle infrastrutture critiche. Il progetto accelererà inoltre lo sviluppo e la convalida di nuovi algoritmi di IA da parte del consorzio e della più ampia comunità dell’intelligenza artificiale. A tal fine, si avvarrà di ambienti digitali open-source compatibili con l’intelligenza artificiale in grado di simulare scenari realistici che coinvolgono il funzionamento di sistemi fisici e il processo decisionale umano. Infine, AI4REALNET contribuirà ad affrontare gli aspetti critici della decarbonizzazione, della digitalizzazione e della resilienza.
Obiettivo
The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities. It has two main strategic goals: 1) to develop the next generation of decision-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with the resilience, safety, and security of critical infrastructures as core requirements, and 2) to boost the development and validation of novel AI algorithms, by the consortium and AI community, through existing open-source digital environments capable of emulating realistic scenarios of physical systems operation and human decision-making.
The core elements are: a) AI algorithms mainly composed by supervised and reinforcement learning, unifying the benefits of existing heuristics, physical modelling of these complex systems and learning methods, as well as, a set of complementary techniques to enhance transparency, safety, explainability and human acceptance; b) human-in-the-loop decision making for co-learning between AI and humans, considering integration of model uncertainty, human cognitive load and trust; c) autonomous AI systems relying on human supervision, embedded with human domain knowledge and safety rules.
The AI4REALNET framework will be validated in 6 uses cases driven by industry requirements, across 3 network infrastructures with common properties. The use cases are focused on critical challenges and tasks of network operators, considering strategic long-term goals, such as decarbonisation, digitalisation, and resilience to disturbances, and are formulated in a unified sequential decision problem where many AI and non-AI algorithms can be applied and benchmarked.
Campo scientifico
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologycivil engineeringtransportation engineering
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectrical engineeringpower engineering
- social sciencessocial geographytransporttransport planningair traffic management
- natural sciencescomputer and information sciencesartificial intelligenceheuristic programming
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinatore
4200 465 Porto
Portogallo